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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
From the tragic incident in Hanoi to the question: What does it means to be civilized?
Published:
This reflection is inspired by the recent tragic incident in Hanoi. On September 12, 2023, at around 23:30 ICT (UTC+07:00), a devastating fire broke out in a nine-story microapartment building. Of the approximately 150 residents, 56 lost their lives and 37 were injured. My heartfelt condolences go out to the victims and their families. Here, I share my thoughts in both Vietnamese (first) and English.
How to prepare a profile to apply for doctoral studies
Published:
This is a recollection of my own experience when applying for a doctoral study opportunity in the US. I hope it can serve as either a guide or a reference to help you prepare your profile and application for doctoral studies not only in the US but also other countries. This recollection is written in two languages: Vietnamese (display first) and English.
portfolio
How to learn English in an effective manner (In Progress)
Published:
This is a collection of my proposed lessons for self-learning English in a structured and effective manner. Here are several disclaimers about this collection. Firstly, this collection is targeted towards learners of Academic English preparing for standardized language proficiency tests such as IELTS. Secondly, most of the lessons here are created with the intention of preparing the readers for IELTS. However, even those not preparing for IELTS may find the lessons useful. Finally, the lessons are written in both English and Vietnamese, making it a great reference for Vietnamese learners struggling with the language.
Linear Algebra for Machine Learning and Data Science
Published:
This is a collection of my projects and notes for learning and reviewing important linear algebra for the journey of masterning Machine Learning and Data Science.
Optimization for Machine Learning and Data Science
Published:
This is a collection of my projects and lecture notes, designed to enhance learning and solidify my understanding of convex optimization concepts crucial for mastering Machine Learning and Data Science.
Probability and Statistics for Machine Learning and Data Science
Published:
This is a collection of my projects and lecture notes, designed to enhance learning and solidify my understanding of essential probability and statistics concepts crucial for mastering Machine Learning and Data Science.
publications
Measurement setup for differential spectral responsivity of solar cells
Published in Optical Review, 2020
A setup for measuring differential spectral responsivities of unifacial and bifacial solar cells under bias light conditions.
Recommended citation: Kärhä, P., Baumgartner, H., Askola, J. et al. Measurement setup for differential spectral responsivity of solar cells. Opt Rev 27, 195–204 (2020).
talks
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Published:
The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry’s manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
Published:
Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate uncertainties and complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to privacy, computational, and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages the split learning paradigm and variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of large-scale, distributed memory-based experiments, we demonstrate the broad applicability of SplitVAEs in three distinct domain areas: power systems, industrial carbon emissions, and supply chains. Our experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Our results show that SplitVAEs outperform conventional state-of-the-art methodologies and provide a superior, computationally efficient, and privacy-compliant alternative to scenario generation.
teaching
How to make frames and infer people - A lecture in Optics and Computer Vision
Guest Lecture - Undergraduate Course, RMIT University Vietnam, Department of Electrical Engineering, 2023
In this lecture, we would delve deeply into the intricate anatomy of the human eye, exploring how our understanding of its structure and function has been instrumental in the development of sophisticated cameras. Furthermore, we would examine the key operating principles of cameras that empower us to perform complex computer vision tasks.